Download Assessing the impacts of global warming on meteorological hazards

Assessing the impacts of global warming on meteorological
hazards and risks in Japan: Philosophy and achievements of the
SOUSEI program
Tetsuya Takemi1, Yasuko Okada2, Rui Ito3, Hirohiko Ishikawa1, and Eiichi Nakakita1
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Disaster Prevention Research Institute, Kyoto University, Japan
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Japan Agency for Marine-Earth Science and Technology, Japan
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National Research Institute for Earth Science and Disaster Resilience, Japan
Correspondence to: Tetsuya Takemi, Disaster Prevention Research Institute, Kyoto University,
Gokasho, Uji, Kyoto 611-0011, Japan. E-mail: [email protected]
Abstract:
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We review the philosophy and achievements of the research activity on assessing the
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impacts of global warming on meteorological hazards and risks in Japan under Program for
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Risk Information on Climate Change (SOUSEI). The concept of this research project consists
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of assessing worst-class meteorological hazards and evaluating probabilistic information on
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the occurrence of extreme weather phenomena. Worst-scenario analyses for historical extreme
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typhoons and probabilistic analyses on Baiu, warm-season rainfalls, and strong winds with the
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use of high-performance climate model outputs are described. Collaboration among the fields
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in meteorology, hydrology, coastal engineering, and forest science plays a key role in
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advancing the impact assessment of meteorological hazards and risks. Based on the present
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research activity, possible future directions are given.
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KEYWORDS
meteorological hazard; extreme weather; global warming; impact
assessment
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INTRODUCTION
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Extreme weather phenomena such as tropical cyclones (TCs), heavy rainfall, and strong
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winds have profound impacts on social infrastructure and human society. Such extreme
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phenomena are meteorological hazards that sometimes spawn disasters. Climate change is
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considered to influence the frequency and severity of extreme weather phenomena, as detailed
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in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change
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(IPCC) (IPCC, 2013). Based on the future projections, disaster risks by TCs are anticipated to
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increase globally (IPCC, 2014).
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In assessing disaster risks, better estimates on meteorological hazards are necessary.
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Because the occurrence of disasters depends on the local characteristics of geographic,
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atmospheric, artificial, and social environment, meteorological hazards should also be
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examined at local scales. In addition, from a viewpoint of disaster risk assessment, it is very
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important to estimate quantitatively the severity of meteorological hazards such as rainfall
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amount and wind speed, because disaster occurs when the intensity and/or duration of
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extreme weather exceed a certain extreme threshold. We should also be aware of the fact that
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rainfalls and winds critically depend on local topographic features and relative distances from
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meteorological disturbances. Furthermore, probability estimates on the development of
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meteorological disturbances should also be provided in order to assess the probability for the
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occurrence of extreme weather.
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Based on these considerations, one group, named “Risk Assessment of Meteorological
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Disasters under Climate Change”, in Theme-D “Precise Impact Assessments on Climate
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Change” under Program for Risk Information on Climate Change (the SOUSEI program
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during FY2012-FY2016) is designed to assess the impacts of climate change on
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meteorological hazards and risks over Japan. Here we describe the philosophy and concept of
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the meteorological disaster group under the SOUSEI program. We then review the
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achievements of the present research project and discuss the current status by comparing the
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results with those in other studies.
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RATIONALE
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The present research concept consists of evaluating worst-class meteorological hazards and
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estimating probabilistic information on the occurrence of extreme phenomena. For these
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purposes, we use the data from climate model simulations for the present climate and future
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climate conditions conducted under Coupled Model Intercomparison Project Phase 5
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(CMIP5) (Tayler et al., 2012), Innovative Program of Climate Change Projection for the 21st
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Century (KAKUSHIN) (Kitoh et al., 2009), and the SOUSEI program. Data both from
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general circulation models (GCMs) and regional climate models (RCMs) are used. Figure 1
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shows the concept of the present research project under the SOUSEI program. Table SI in the
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Supplement lists the participants of the meteorological disaster group. The impacts of
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meteorological hazards on local disasters are assessed through collaborating with the
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hydrological and coastal research groups (Mori et al., 2016).
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In Japan, typhoons, extra-tropical cyclones, stationary fronts, and thunderstorms are major
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meteorological hazards that would spawn disasters. Based on the statistics on natural disasters
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by Cabinet Office (2015), typhoons and frontal rainfalls are the ones among the worst-class
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hazards in Japan. Actually, typhoons and frontal rains are ranked at higher spots in Japan as
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producing costly insurance losses among all the meteorological disasters during the period
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from 1970 to 2015 (Swiss Reinsurance Company, 2016). Thus, we focus on typhoons and
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frontal rainfalls.
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In considering worst-class meteorological hazards, past disaster-spawning events are
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regarded as a baseline. In Japan, Typhoon Vera (1959) (so called Isewan Typhoon) caused
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devastating damages including more than 5000 fatalities, while Typhoon Mireille (1991)
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caused the most costly insurance loss among the TCs in the Pacific region during the period
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from 1970 to 2015 (Swiss Reinsurance Company, 2016). Evaluating the effects of climate
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change on the severity of typhoons is primarily important to take measures for preventing and
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mitigating natural disasters under global warming. During the SOUSEI program, we conduct
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quantitative analysis on the climate change impacts on Typhoon Vera (1959), Typhoon
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Mireille (1991), Typhoon Songda (2004), Typhoon Talas (2011), and Typhoon Haiyan (2013).
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Although there are other damaging typhoons in Japan before Typhoon Vera (1959) such as
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Muroto Typhoon (1934), Makurazaki Typhoon (1945), Typhoon Kathleen (1947), and
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Typhoon Marie (1954) (known as Toyamaru Typhoon) (National Astronomical Observatory
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of Japan, 2015), the cases examined are limited to the typhoons after 1958. This is because the
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data used as the initial and boundary conditions for the numerical simulations are the
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long-term reanalysis data of Japan Meteorological Agency, called JRA-55 (Ebita et al., 2011;
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Kobayashi et al., 2015), which are available after 1958.
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To assess the impacts of climate change on specific worst-class events, it is useful to
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estimate the difference in the climatological atmospheric conditions between the present and
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the future climate. For this purpose, a dynamical downscaling approach with an RCM plays a
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critical role in quantitatively representing extreme weather through resolving physical
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processes and topographical features at a high resolution. For example, Kanada et al. (2010)
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clearly indicated that the rainfall in a 5-km-mesh RCM is better represented than that in the
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20-km-mesh GCM. Tsuboki et al. (2015) conducted 2-km-mesh downscaling experiments for
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the 30 strongest typhoons simulated in 20-km-mesh Atmospheric GCM (AGCM) for future
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warmed climate and obtained 12 supertyphoons, which cannot be quantitatively reproduced in
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AGCMs.
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In contrast, it is not obvious whether a specific storm in the present climate is also
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reproduced in a future climate as a storm with changes in its intensity but without any
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pronounced changes in its track as well as the genesis location. Furthermore, a past specific
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event will not be reproduced in the present-climate runs; this is an issue related to realization
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in climate simulations. Thus, if we consider worst-class hazards based on the past extreme
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events, we need to take another method in addition to dynamical downscaling.
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An effective method to assess the impacts of climate change on a specific event is a
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pseudo-global warming (PGW) experiment developed by Schär et al. (1996) and Sato et al.
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(2007). The PGW experiment is designed to add climate change components to the analysis
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fields of the past events. Climate change components are defined as the increments of the
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future climate from the present climate in GCM runs. The performance and reliability of the
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PGW method was verified for the atmospheric condition and precipitation over East Asia in
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June under climate change (Yoshikane et al., 2012). The PGW method has been applied for
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various types of weather phenomena including Baiu rainfall (Kawase et al., 2009), snowfall in
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Japan (Hara et al., 2008), marine boundary layer clouds over the eastern Pacific (Lauer et al.,
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2010), a severe flooding event over the United States (Lackmann, 2013), winter precipitation
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in Colorado (Rasmussen et al., 2011), and tornadic storm events over the United States (Trapp
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and Hoogewind, 2016). Lauer et al. (2013) examined the uncertainties of the PGW method
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using multiple CMIP5 models in assessing climate change impacts in the Hawaii region and
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successfully identified robust signals of future changes in the Hawaii climate. These studies
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investigated the climate change impacts for persistent anomalous weather.
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In contrast, there are few studies that examined the impacts of climate change on a specific
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extreme event. For example, Lackmann (2015) estimated the impacts of climate change on
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Hurricane Sandy (2012). Takayabu et al. (2015) investigated, by conducting ensemble
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downscaling simulations under the actual and the pre-industrial condition, the effects of
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global warming on the storm surge induced by Typhoon Haiyan (2013) and showed that the
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worst-class storm surge will become severer under global warming. In this way, the PGW
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method is currently being applied for analyzing an extreme event. One possible shortcoming
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of the PGW method for an extreme event analysis would be the arbitrariness in the choice of
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the PGW increments. Climatological mean PGW increments may not always be adequate in
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determining the environmental conditions for extreme events. This issue is still an open
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question for future studies.
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In the present studies, we employ the PGW experiment approach to investigate the climate
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change impacts on specific extreme events. To reproduce past events, we use the long-term
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reanalysis dataset, JRA-55. Dynamical downscaling and PGW experiments are conducted
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with the use of the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008).
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Probability information on the occurrence of meteorological hazards provides
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quantitatively confidence and/or uncertainties for projected changes in extreme events. To
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evaluate statistical significance, a large number of projection runs (on the order of 100 or
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more) are required. Moreover, high-resolution data (on the order of 1 km) are desirable to
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evaluate quantitatively the impacts at regional scales. However, owing to the limitation of
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computational resources, it is still not possible to meet the needs on both sample size and
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spatial resolution. Therefore, we currently take priority in high-resolution over sample size by
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primarily using the 20-km-mesh AGCM simulations (MRI-AGCM version 3.2, Mizuta et al.,
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2012, 2014; Kitoh et al., 2016) and the downscaled 5-km-mesh simulations with the
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Non-Hydrostatic RCM (NHRCM) (Nakano et al., 2012; Murata et al., 2015). High-resolution
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is important since the representation of topography and rainfall amount/wind speed critically
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depends on how topography is reproduced at the model resolutions (Takemi, 2009; Oku et al.,
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2010). The 20-km-mesh AGCM data are primarily used for analyzing atmospheric conditions
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and circulations, while the 5-km-mesh RCM data are used for quantitative assessment of
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rainfalls and winds. In addition, we also use an ensemble of 60-km MRI-AGCM runs with
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multiple cumulus schemes and multiple SST patterns in order to obtain statistical information.
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The future climate with the MRI-AGCM are under the Representative Concentration
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Pathways (RCP) 8.5 scenario.
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The advantage in using the MRI-AGCM data is emphasized here for its highest-level
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performance. Kusunoki (2016) demonstrated that the 20-km-mesh and 60-km-mesh
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MRI-AGCM runs provide better performance in reproducing precipitation, especially in
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summer, and the seasonal march of Baiu front over East Asia than those obtained from the
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CMIP5 GCMs. Therefore, although the MRI-AGCM runs were conducted only for a single
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future scenario, i.e., RCP8.5, we consider that the better performance of MRI-AGCM gives
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better reliability in assessing the impacts of climate change on meteorological hazards.
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Another point to note in using high-resolution data is related to model numerics. In general,
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meteorological models include various types of numerical filters and diffusions and thus may
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not accurately resolve physical phenomena exactly at the model grid. The resolution that can
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effectively resolve physical phenomena is considered to be about 6 times the grid spacing or
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greater (Takemi and Rotunno, 2003; Skamarock, 2004; Bryan, 2005). Furthermore,
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considering that typhoons and heavy rainfalls are generated by cumulus activity, resolving
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non-hydrostatic effects (Weisman et al., 1997) is also important. From these considerations, it
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is emphasized that a resolution of a few kilometers (so called convection-permitting
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resolution, Trapp et al., 2007; Zhang et al., 2007) is at least necessary for evaluating
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quantitatively meteorological hazards in regional scales. Therefore, in this research project,
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the 5-km NHRCM data are mainly used, and downscaling experiments at grids of one or a
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few kilometer are conducted.
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RESULTS FROM THE SOUSEI PROGRAM
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Worst-scenario analysis
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Worst-case analysis has been conducted for Typhoon Vera (1959), Typhoon Mireille (1991),
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Typhoon Songda (2004), and Typhoon Talas (2011), which caused significant disasters over
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Japan within the past 60 years or so.
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Since rainfall amount and wind speed induced by typhoons critically depend on their tracks,
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examining how rain and wind are sensitive to the typhoon tracks is important to identify worst
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tracks for spawning disasters. Ishikawa et al. (2013) proposed a methodology to control
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typhoon tracks by extracting and relocating typhoon vortices that are separated from the
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background field through a potential vorticity (PV) inversion technique (Davis and Emanuel,
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1991). With this methodology, we are able to generate a large number of typhoon ensembles
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with different tracks and to identify a typhoon track that produces the most significant hazard
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as the worst scenario. Oku et al. (2014) applied the PV inversion methodology in generating
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typhoon ensembles to investigate the maximum probable rainfall over the Kii Peninsula
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produced by Typhoon Talas (2011).
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Typhoon Vera (1959) has been extensively investigated in the present research project.
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Shimokawa et al. (2014) developed a new typhoon bogusing method based on the PV
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inversion technique to control the track of a simulated typhoon and applied the method to
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investigate the impacts of global warming on the storm surge due to Typhoon Vera (1959).
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Their method was extended by Murakami et al. (2015) who evaluated the risk of coastal
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disaster resulting from the multiple hazards due to a Vera-class typhoon and showed that the
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middle part of Ise Bay is more dangerous than the inner part of Ise Bay.
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Mori and Takemi (2016) and Takemi et al. (2016a) conducted PGW experiments for
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Typhoon Vera (1959) by prescribing monthly-mean warming increments from 4 ensembles of
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the 20-km AGCM runs (Mizuta et al., 2014) on the JRA-55 analysis fields of September 1959.
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In determining the PGW conditions, the relative humidity increment was not added, because
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of no significant future change in relative humidity (Takemi et al., 2012). The wind increment
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was also not added, because differences in wind fields largely change typhoon tracks and
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negatively affect the impact assessments on natural hazards (Mori et al., 2014). It was
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demonstrated that the typhoons at the times of their maximum intensity and landfall are
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unanimously intensified under the PGW condition. The robustness of the intensification of
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this extreme typhoon has been further investigated through multi-model inter-comparisons
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(Kanada et al., 2016).
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Typhoon impacts are also investigated through collaborating with forest scientists. Forest
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trees play an important role in determining surface heat/moisture fluxes to the atmosphere and
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thereby controlling water cycle. Furthermore, forest trees are one of the important players in
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the global carbon budget. Thus, assessing the damaging impacts of typhoons on forest trees is
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an important issue in forest sciences. According to Takano et al. (2016), Typhoon Marie
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(1954) caused the severest damage to forest trees in the record history of Japan. However, due
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to the availability of JRA-55, we focused on Typhoon Songda (2004), which took a track
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similar to that of Typhoon Marie and induces severe damages to forest trees at many places in
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Hokkaido (Sano et al., 2010; Hayashi et al., 2015). Ito et al. (2016) examined the influences
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of global warming on the severity of Typhoon Songda (2004) over Hokkaido and
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demonstrated that wind speed over Hokkaido decreases under the PGW conditions, owing to
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the rapid weakening of the future typhoons in the higher-latitude regions despite the
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strengthening at the typhoons’ maximum intensity in the lower latitudes. The rapid weakening
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of the future typhoons at higher latitudes is due to the weakening of baroclinicity under global
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warming. Takemi et al. (2016b) further investigated the latitudinal dependence of the change
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in typhoon intensity through the PGW experiments for Typhoon Mireille (1991) and indicated
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that typhoon winds will be intensified in Kyushu (the southern part of Japan) and be
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weakened in Tohoku (the northern part of Japan). Takano et al. (2016) used the output of the
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numerical simulations for Typhoon Songda (2004) by Ito et al. (2016) to investigate the
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changes in the damages to forest trees in Hokkaido under global warming. Further analyses on
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forest damages in Kyushu and Tohoku by Typhoon Mireille (1991) are now being undertaken.
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Probabilistic analysis
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Probability information is important to evaluate the significance and uncertainty of the
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occurrence of extreme events. Although the ensemble number of the MRI-AGCM runs is not
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sufficient to derive reliable probabilities, uncertainty and robustness of the projected changes
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are derived.
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Okada et al. (2016) used the outputs from the present-climate simulation and the 4
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ensemble future-climate projections from MRI-AGCM to investigate the projected changes in
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atmospheric circulation during the Baiu season. They indicated the delayed northward shift of
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the Baiu front in June and the resulting decrease in rainfall in western Japan in June.
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According to the results with different SST conditions, they found that the projected changes
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in atmospheric circulation in June have a robust commonality while the changes of
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atmospheric condition in July and August depend on the SST conditions. Thus, the AGCM
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ensembles are necessary to evaluate the robustness and uncertainty of the projected changes.
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Nakakita et al. (2015, 2016a) investigated the characteristics of atmospheric circulation
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relevant to localized heavy rainfall in summer by using the 20-km MRI-AGCM outputs as
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well as the 60-km MRI-AGCM ensemble data. The 5-km RCM outputs were used to
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quantitatively examine the rainfall amount and its relationship with the atmospheric
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circulation identified with the AGCM outputs. They revealed that anti-cyclonic circulation
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originating from the western North Pacific toward the Sea of Japan, which is favorable for the
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rainfall in the western part of Japan, is projected to be more frequent in the future climate and
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that a significant increase in rainfall is found at the 5% significance level on the Japan Sea
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side of the Tohoku region in July and in all regions on the Japan Sea side in August.
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Kuzuha (2015) analyzed the annual maximum series of observed daily precipitation and
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examined probability distributions for fitting the observations. They successfully estimated
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daily precipitation with the 120-year return period for 51 meteorological stations.
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Future changes in strong wind hazards are investigated by Zhang et al. (2014a) from the
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5-km RCM outputs. They showed that wind speeds are projected to increase in southern Japan
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while projected to decrease in central and northern Japan. Because strong winds are primarily
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due to typhoons, stronger winds in the south and weaker winds in the north in the future
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climate seem to be consistent with the latitudinal dependence of the typhoon intensity as
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shown by Ito et al. (2016) and Takemi et al. (2016b). In order to assess the risks of strong
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winds due to typhoons, Nishijima (2016) proposed a framework for decision optimization for
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adaptation of civil infrastructure to climate change by applying a system assessing wind risk
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for residential buildings (Zhang et al., 2014a, 2014b) to multiple climate projections. The
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framework bases on decision graphical representation consisting of four layers that evaluate
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the changes in greenhouse gas concentration, air temperature, hazard, and consequence, with
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each layer being related with each other into a Bayesian network. At this point, Nishijima
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(2016) only provided the concept; however, the framework should provide a pathway to the
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civil infrastructure adaptation to climate change.
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DISCUSSION
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The results from the present research project are summarized in Table I. These results are
evaluated by comparing with those from other studies.
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There are not many studies on extreme typhoons from a worst-scenario perspective. We
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have extensively conducted PGW experiments for some past extreme typhoons and
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inter-model comparisons to gain robust signals in their changes under global warming.
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Furthermore, there have been few studies on the typhoon impacts in northern Japan; we have
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also examined this issue by collaborating with forest scientists.
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Kossin et al. (2014) identified a pronounced poleward migration of the location of TC
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maximum intensity with a rate of 53 km per decade in the Northern Hemisphere, because of
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the poleward expansion of tropical circulation and the associated increase in potential
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intensity to about 30oN latitude. The PGW experiments for Typhoon Vera (1959),
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demonstrating increased intensity at the maximum intensity and the landfall, are consistent
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with the study by Kossin et al. (2014). In contrast, typhoons at higher latitudes (north of 40oN
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latitude) are projected to experience rapid weakening, leading to the reduction of the typhoon
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winds in northern Japan.
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For the probabilistic analyses, the use of the data from the high-performance MRI-AGCM
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(Kitoh et al., 2016; Kusunoki, 2016) and the downscaled RCM (Murata et al., 2015) is the
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advantage of this research project, although the ensemble number is limited and the future
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scenario is only RCP8.5. With the use of the high-performance climate data, we were able to
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provide the changes in the atmospheric circulation during the Baiu period and the
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warm-season rainfall.
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REMARKS AND FUTURE DIRECTIONS
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The philosophy and achievements in the studies of assessing the impacts of global warming
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on meteorological hazards and risks in Japan under the SOUSEI program were reviewed. The
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concept of the research project consists of assessing worst-class meteorological hazards and
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evaluating probabilistic information on the occurrence of extreme phenomena. Collaboration
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among the fields in meteorology, hydrology, coastal engineering, and forest science plays a
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critical role in advancing the impact assessment of meteorological hazards and risks.
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There are still remaining issues. One is to evaluate the worst-case scenario for heavy
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rainfalls in warm season. PGW experiments for extreme rainfalls will be a step forward to
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resolve this issue. The second is to quantitatively assess the probability of extreme events
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from a large number of climate projection samples. In-depth analyses on the data from the
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Database for Policy Decision making for Future climate change (d4PDF) (Shiogama et al.,
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2016) are required. Nakakita et al. (2016b) conducted preliminary analyses on warm-season
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rainfall and atmospheric circulation features using the d4PDF dataset. Furthermore,
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higher-resolution RCM data are desirable to assess the impacts of climate change on
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smaller-scale phenomena such as thunderstorms and the associated heavy rainfalls and strong
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winds, advancing the study on the environmental conditions for summertime local rainfalls in
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Tokyo (Takemi, 2012).
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Recently, heavy-rainfall-producing stationary convective systems have caused severe
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disasters such as the landslide in Hiroshima in August 2014 and the flooding in Ibaraki in
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September 2015. Future changes in the characteristics of such stationary convective systems
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will be a next research issue. Automated identification algorithms for stationary convective
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systems (Unuma and Takemi, 2016a, 2016b) can be applied if high-resolution, high-frequency
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rainfall outputs from climate projections are available, which will provide future projections
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in heavy rainfall due to stationary convective systems under global warming.
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ACKNOWLEDGMENTS
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The reviewers’ comments are acknowledged in improving the original manuscript. This
work was conducted under the SOUSEI program of MEXT.
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SUPPLEMENTS
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Table SI. List of the participants of the meteorological disaster group, “Risk Assessment of
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Meteorological Disasters under Climate Change”, in Theme-D “Precise Impact
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Assessments on Climate Change” under Program for Risk Information on Climate
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Change (the SOUSEI program) during FY2012-FY2016
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List of Figures
Figure 1. Conceptual diagram of the research in the assessment of meteorological hazards and
risks under climate change in the SOUSEI program
22
List of Tables
Table I. Summary of the results of the meteorological disaster group under in Theme-D of the
SOUSEI program. GCM20, GCM60, and RCM5 refer to 20-km-mesh MRI-AGCM,
60-km-mesh MRI-AGCM, and 5-km-mesh RCM, respectively
23
Table I. Summary of the results of the meteorological disaster group under in Theme-D of the
SOUSEI program. GCM20, GCM60, and RCM5 refer to 20-km-mesh MRI-AGCM,
60-km-mesh MRI-AGCM, and 5-km-mesh RCM, respectively
Analysis category
Worst-scenario
analysis
Probabilistic
analysis
Meteorological
hazard
Typhoon
Input dataset
Results
GCM20, JRA-55
Increased intensity of typhoons at their maturity
and the landfall. Typhoon impacts are more
severe in the southern and the Pacific side of
Japan, but may be reduced in northern Japan.
Baiu
RCM5, GCM20,
GCM60, CMIP5
RCM5, GCM20,
GCM60
RCM5
Delayed northward shift of Baiu front. Reduction
of rainfall in June in western Japan.
Increased risks of the occurrence of heavy rainfall
in summer.
Increased risks of strong winds in southern Japan
while decreased risks in central and northern
Japan. Regional characteristics of residential
buildings should be taken into account.
Warm-season
rainfall
Strong wind
24